Korean J Radiol.  2019 Oct;20(10):1431-1440. 10.3348/kjr.2019.0212.

Deep Learning Algorithm for Reducing CT Slice Thickness: Effect on Reproducibility of Radiomic Features in Lung Cancer

• Park S
• Lee SM
• Do KH
• Lee JG
• Bae W
• Park H
• Jung KH
• Seo JB

• KMID: 2459162
• DOI: http://doi.org/10.3348/kjr.2019.0212

Abstract

OBJECTIVE
To retrospectively assess the effect of CT slice thickness on the reproducibility of radiomic features (RFs) of lung cancer, and to investigate whether convolutional neural network (CNN)-based super-resolution (SR) algorithms can improve the reproducibility of RFs obtained from images with different slice thicknesses.
MATERIALS AND METHODS
CT images with 1-, 3-, and 5-mm slice thicknesses obtained from 100 pathologically proven lung cancers between July 2017 and December 2017 were evaluated. CNN-based SR algorithms using residual learning were developed to convert thick-slice images into 1-mm slices. Lung cancers were semi-automatically segmented and a total of 702 RFs (tumor intensity, texture, and wavelet features) were extracted from 1-, 3-, and 5-mm slices, as well as the 1-mm slices generated from the 3- and 5-mm images. The stabilities of the RFs were evaluated using concordance correlation coefficients (CCCs).
RESULTS
The mean CCCs for the comparisons of original 1 mm vs. 3 mm, 1 mm vs. 5 mm, and 3 mm vs. 5 mm images were 0.41, 0.27, and 0.65, respectively (p < 0.001 for all comparisons). Tumor intensity features showed the best reproducibility while wavelets showed the lowest reproducibility. The majority of RFs failed to achieve reproducibility (CCC ≥ 0.85; 3.6%, 1.0%, and 21.5%, respectively). After applying the CNN-based SR algorithms, the reproducibility significantly improved in all three pairings (mean CCCs: 0.58, 0.45, and 0.72; p < 0.001 for all comparisons). The reproducible RFs also increased (36.3%, 17.4%, and 36.9%, respectively).
CONCLUSION
The reproducibility of RFs in lung cancer is significantly influenced by CT slice thickness, which can be improved by the CNN-based SR algorithms.

Keyword

Computed tomography; Radiomics; Slice thickness; Deep learning

MeSH Terms

Learning*
Lung Neoplasms*
Lung*
Retrospective Studies

Figure

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  Fig. 1 Flow diagram for patient inclusion.GGN = ground-glass nodule

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  Fig. 2 Development of CNN algorithm for image conversion.A. Network consists of preprocessing, non-linear mapping, and reconstruction parts. Preprocessing part deals with variance from input images with different scales, and non-linear mapping and reconstruction parts deal with differences between input images at low resolution (3 or 5 mm) and target image (1 mm). B. Ground-truth image, converted image, and difference map are presented. In difference map, green indicates zero, blue represents negative values, and red indicates positive values. When comparing original 1-mm and SR 3-mm images, most of lung parenchyma and small vascular structures show near-zero difference. Regions showing residual differences are present in pulmonary nodule, large vascular structures, fissure, and border between lung parenchyma and pleura (root-mean-square error: 30.83 HU). C. Comparison between original 1-mm and SR 5-mm images shows slightly more prominent differences in background lung parenchyma, including vascular structures, with speckled error regions remaining along margin of lung nodule (root-mean-square error: 37.65 HU). SR 3 mm refers to images converted from original 3-mm slice thickness, SR 5 mm refers to images converted from original 5-mm slice thickness. CNN = convolutional neural network, SR = super-resolution, 3D = three-dimensional

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  Fig. 3 Proportion of reproducible RFs (CCC ≥ 0.85) in different slice-thickness pairings before and after application of image conversion.A. Majority of RFs failed to reach cut-off for reproducibility (CCC ≥ 0.85) in original images. After image conversion (B), proportion of reproducible RFs markedly increased in all three slice-thickness pairings. Similar tendency was observed using alternative cut-off values of 0.80 and 0.90. CCC = concordance correlation coefficients, RF = radiomic feature, SR = super-resolution

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